ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer

TL;DR

ModeT introduces a novel Transformer-based approach for medical image registration that explicitly models multiple deformation modes and outperforms existing methods on brain MRI datasets.

Contribution

The paper proposes ModeT, a Transformer architecture that explicitly decomposes and models multiple motion modalities for improved deformable image registration.

Findings

Outperforms state-of-the-art registration networks

Effectively models multiple motion modes

Demonstrates superior performance on brain MRI datasets

Abstract

The Transformer structures have been widely used in computer vision and have recently made an impact in the area of medical image registration. However, the use of Transformer in most registration networks is straightforward. These networks often merely use the attention mechanism to boost the feature learning as the segmentation networks do, but do not sufficiently design to be adapted for the registration task. In this paper, we propose a novel motion decomposition Transformer (ModeT) to explicitly model multiple motion modalities by fully exploiting the intrinsic capability of the Transformer structure for deformation estimation. The proposed ModeT naturally transforms the multi-head neighborhood attention relationship into the multi-coordinate relationship to model multiple motion modes. Then the competitive weighting module (CWM) fuses multiple deformation sub-fields to generate the resulting deformation field. Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets show that our method outperforms current state-of-the-art registration networks and Transformers, demonstrating the potential of our ModeT for the challenging non-rigid deformation estimation problem. The benchmarks and our code are publicly available at https://github.com/ZAX130/SmileCode.